SANTA CLARA, Calif.--(BUSINESS WIRE)--As an engineer, I love solving problems and using the “language of math”
– or numbers – to understand the world we live in. With meaning beyond
their stated numerical value, numbers add context to stories and
challenges in a way that words alone cannot. Big numbers are interesting
as their meaning is often much more complex than their sheer size might
suggest. With one number in particular – 4 terabytes (TB) – this is
especially true, and I’m excited about the meaning behind that number
for the autonomous driving industry.

First things first: Why that number? Four terabytes is the estimated
amount of data that an autonomous car will generate in about an hour and
a half of driving – or the amount of time a typical person spends in
their car each day. By 2020, that’s also the amount of data that 3,000
individual internet users are expected to put out each and every day. It
might not sound like much until you think of it in a different way: How
many of us have 3,000 friends on Facebook? Now imagine trying to follow
and absorb everything they all post each and every single day.

If the interesting thing about the data created by a self-driving car
was simply the amount of it, 4TB wouldn’t be very exciting. What makes “data
the new oil” for autonomous driving – and what makes it a real
challenge – is our need to make sense of that data, to turn it into
actionable insight that lets cars think, learn and act without human
intervention. Data that lets cars do the driving so that the 90
percent of the accidents caused by human error1 may one
day be a thing of the past.

Intel is a data company. We know how to create, move, store, process,
analyze and manage data – at massive scale – and we’re applying this
vast expertise to the autonomous driving industry. From experience, we
also know the fastest way to solve the autonomous driving data challenge
is through industry collaboration. While there’s a lot of work to do to
deliver fully autonomous vehicles by 2021, I am confident that by
working with the industry and our partners, together we can get it done.

Technical data is perhaps most obvious. This data comes from a suite of
sensors and is the car’s “view” of the world immediately around it. This
data helps the car recognize a person or fire hydrant, “see” a new
pothole, or maybe calculate how quickly a nearby car is approaching.
This kind of technical data is also great for capturing new driving
scenarios and pushing it to the cloud for learning and improving the
software that controls driving behavior. When this kind of data goes to
the cloud, it becomes incredibly valuable to other vehicles connected
with that same cloud.

Crowdsourced data is something that a community of local cars takes in
from their surroundings, such as traffic or changes to the road
conditions. You can imagine all kinds of cool applications that could
use this kind of information, such as finding a nearby parking spot or
avoiding traffic jams.

Finally, there is personal data, including the radio stations you like
to listen to, coffee shops you frequent, routes you prefer and so on.
This type of data could be useful in creating the most amazing
personalized experience in your autonomous vehicle.

As the industry moves toward fully autonomous cars, data presents a
number of challenges for the entire global industry. The first challenge
goes back to that original number: 4TB. The exponentially growing size
of the data sets necessitates an enormous amount of compute capacity to
organize, process, analyze, understand, share and store. Think data
center server compute power, not PC power.

The need to train autonomous vehicles as quickly as possible presents
another challenge. When new driving responses or situations are
identified, machine learning, simulation and algorithm improvements must
happen almost instantly – not weeks or months later – and updated
driving models must be pushed to the cars immediately once available.
When, where and how that happens has implications not just for today,
but for the day when self-driving cars are the norm.

There’s also the matter of data protection and what that means for
consumers to eventually trust the autonomous experience. How we will
achieve truly secure storage and sharing of data is a question I am
asked about frequently and one we take very seriously. Which data gets
stored? Which gets tossed? Which data sets get shared? And how will we
protect it all? These are valid questions that will require industry
collaboration and our best experts to address in a meaningful way.

Finally, the data challenge grows over time as small fleets of vehicles
eventually become hundreds of millions of vehicles. The ability to make
this happen comes only through the ability to process increasingly
larger data sets. True system scalability will be critical both inside
our cars – back to that 4TB number – and outside our cars in massive
data centers, as the self-driving supercomputer and the cloud that
supports it continue to evolve.

No one company can tackle these data challenges on its own. At Intel, we
believe the best way to solve the autonomous driving data challenge is
to do it collectively, to work together across the industry to develop
secure state-of-the-art platforms and to share safety-related
information. As we work toward a shared vision of a world without
accidents and with mobility for all, industry collaboration will
accelerate our ability to deliver. I am thrilled to be working with our
Intel team and key partners on the 4TB challenge, as I know that solving
this problem will lead to safer roads and a better journey for all.

Kathy Winter is vice president and general manager of the Automated
Driving Solutions Division at Intel Corporation. She joined Intel in
2016 from Delphi, where she engineered the first cross-country drive of
a fully autonomous vehicle.